Clinomic AI Lab

Pioneering the Future of Critical Care.

Through the synergy of ICU expertise and cutting-edge artificial intelligence, we are committed to advancing research and solutions that elevate the standard of care for critically ill patients across the globe.

The Clinomic AI Lab is Clinomic’s core research department, focusing on Artificial Intelligence and IoT applications at the patient bedside.

In tandem with partner academic institutions, we not only expand our research but also our intellectual property portfolio, ensuring we solidify our position at the cutting edge of medical AI innovations. Each endeavor is rooted in our commitment to patient well-being, ethical responsibility, and competitive excellence. Through our collective efforts, we envision a future where ICU care is seamlessly enhanced by the precision of artificial intelligence.

Members of the AI Lab

Ahmed Hallawa

Head of Department

Dr. Jubin Shah

Research Product Manager

Roney Mathew

Machine Learning Engineer

Cagatay Sariman

Machine Learning Engineer

Hendrik Laux

Machine Learning Engineer, Visual Processing

Maike Gronholz

Medical Working Student

Dr. Arne Peine, MHBA

Medical Advisor

Priv.-Doz. Dr. Lukas Martin, MHBA

Scientific Advisor

Our research principles

Interdisciplinary Synergy

Fostering partnerships between AI specialists, medical experts, and academic researchers to drive innovative solutions in critical care.

Data focus

Employing rigorous, data-driven research approaches to ensure the highest standards of clinical relevance and accuracy.

Responsibility

We approach data by the highest standards of patient privacy, trust, and transparency, reflecting our unwavering dedication to responsible advancements.

“In the intricate synergy of data and clinical decisions within the ICU, we harness technology not merely as a tool, but as an ally. Each innovation we champion is a testament to our commitment to better outcomes and brighter tomorrows.”

Ahmed Hallawa
Head of the Clinomic AI Lab

Publications and Conferences

Nature Partner Journal Digital Medicine

Development and Validation of a Reinforcement Learning Algorithm to Dynamically Optimize Mechanical Ventilation in Critical Care

Nature Scientific Reports

Two-Stage Visual Speech Recognition for Intensive Care Patients

Emerging Microbes and Infections

Perception of the 2020 SARS-CoV-2 Pandemic among Medical Professionals in Germany

JMIR Medical Informatics

Telemedicine in Germany during the COVID-19 Pandemic: Multi-Professional National Survey.Only 135 characters allowed.

EvoStar

On the Use of Evolutionary Computation for In-Silico Medicine: Modelling Sepsis via Evolving Continuous Petri Nets.” In Applications of Evolutionary Computatio

Pneumologie

Perception of the COVID-19 Pandemic among Pneumology Professionals in Germany

JMIR Medical Informatics

Standardized comparison of voice-based information and documentation systems to established systems in Intensive Care: A Cross-Sectional study.

JMIR Medical Informatics

Predicting abnormalities in laboratory values for patients in the intensive care unit using deep artificial neural networks

European Urology Focus

Impact of the COVID-19 Pandemic on Urologists in Germany

Anästhesiologie & Intensivmedizin

Artificial Intelligence and Machine Learning in Intensive Care Research and Clinical Application.

International Journal of Computational Intelligence Systems

A Novel Hybrid Methodology for Anomaly Detection in Time Series. International Journal of Computational Intelligence Systems

AINS

Artificial Intelligence: Challenges and Applications in Intensive Care

Der Anaesthesist

Was Ist Neu… Einsatz von Künstlicher Intelligenz in der Intensivmedizin.

JMIR Medical Informatics

A Deep Learning Approach for Managing Medical Consumable Materials in Intensive Care Units via Convolutional Neural Networks: Technical Proof-of-Concept Study

MECO

A Machine Learning Approach for the Classification of Disease Risks in Time Series

MIPRO

A Novel NLP-Fuzzy System Prototype for Information Extraction from Medical Guidelines.

IEEE SAMI

Incremental Parameter Estimation of Stochastic State-Based Models.

Big Data Analytics for Cyber-Physical Systems

Machine Learning in Future Intensive Care—Classification of Stochastic Petri Nets via Continuous-Time Markov Chains

GECCO

Evo-RL: Evolutionary-Driven Reinforcement Learning

GECCO

Exploration of unknown environments via evolution of behavioral and morphological properties of miniaturized sensory agents

News from the Clinomic AI Lab

Using Clinical Data and AI to reshape COPD – fractional dynamics deep learning models

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VentAI: The Future of Mechanical Ventilation for Critically Ill Patients

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New paper: Artificial intelligence and machine learning in intensive care research and clinical application

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We thank the supporters of our research work

nvidia Inception Program

Join the mission

Join the Clinomic AI Lab

Want to make a real impact on patients’ lives? We are hiring brilliant minds from all scientific backgrounds. Send us your CV and we will contact you as soon as possible. If you have any questions, write us an email to careers@clinomic.ai